Abstract

In-degree, PageRank, number of visits and other measures of Web page
popularity significantly influence the ranking of search results by
modern search engines.
The assumption is that popularity is closely correlated
with quality, a more elusive concept that is difficult to measure directly.
Unfortunately, the correlation between popularity and quality is very weak
for newly-created pages that have yet to receive many visits and/or
in-links. Worse, since discovery of new content is largely done by
querying search engines, and because users usually focus their attention on the
top few results, newly-created but high-quality pages are effectively
``shut out,'' and it can take a very long time before they become
popular.

We propose a simple and elegant solution to this problem: the introduction of a
controlled amount of randomness into search result ranking methods. Doing so offers
new pages a chance to prove their worth, although clearly using too much randomness
will degrade result quality and annul any benefits achieved. Hence there is a
tradeoff between exploration to estimate the quality of new pages and
exploitation of pages already known to be of high quality.
We study this tradeoff both analytically and via simulation, in the context of
an economic objective function based on aggregate result quality amortized over time.
We show that a modest amount of randomness leads to improved search results.